MIRI_AB2Slice

class jwst.transforms.MIRI_AB2Slice(beta_zero=Parameter('beta_zero', value=0.0), beta_del=Parameter('beta_del', value=1.0), channel=Parameter('channel', value=1.0), **kwargs)

Bases: astropy.modeling.core.Model

MIRI MRS alpha, beta to slice transform

Parameters

• beta_zero (float) –

• beta_del (float) –

Attributes Summary

beta_del	Beta_del parameter
beta_zero	Beta_zero parameter
channel	one of 1, 2, 3, 4
fittable
n_inputs	The number of inputs.
n_outputs	The number of outputs.
param_names	Names of the parameters that describe models of this type.
standard_broadcasting

Methods Summary

__call__(*inputs[, model_set_axis, …])	Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.
evaluate(beta, beta_zero, beta_del, channel)	Evaluate the model on some input variables.

Attributes Documentation

beta_del = Parameter('beta_del', value=1.0)

Beta_del parameter

beta_zero = Parameter('beta_zero', value=0.0)

Beta_zero parameter

channel = Parameter('channel', value=1.0)

one of 1, 2, 3, 4

Type

MIRI MRS channel

fittable = False

n_inputs = 1

The number of inputs.

n_outputs = 1

The number of outputs.

param_names = ('beta_zero', 'beta_del', 'channel')

Names of the parameters that describe models of this type.

The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.

When defining a custom model class the value of this attribute is automatically set by the Parameter attributes defined in the class body.

standard_broadcasting = False

Methods Documentation

__call__(*inputs, model_set_axis=None, with_bounding_box=False, fill_value=nan, equivalencies=None, inputs_map=None, **new_inputs)

Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.

static evaluate(beta, beta_zero, beta_del, channel)

Evaluate the model on some input variables.